axis : int Axis along which the cumulative product is computed. dtype : dtype, optional Type of the returned array, as well as of the accumulator in which the elements are multiplied. If *dtype* is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype ...
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size(axis) - value) # Create broadcastable mask mask_start = (min_value.long())[..., None, None].float() mask_end = (min_value.long() + value.long())[..., None, None].float() mask = torch.arange(0, specgrams.size(axis)).float() # Per batch example masking specgrams = ...
PyTorch - torch.gather flyfish 这是一篇让您能懂的torch.gather的文章,下面的例子比官网更容易说明该函数的用法 函数的作用,沿dim指定的轴收集值(Gathers values along an axis specified by dim),相当于 我们有一个二维表根据指定的索引值,把数据取出来。例如一个索引中存储了二维表中每行的最大值或者最小值...
Reverse (invert) the audio along the time axis similar to random flip of an image in the visual domain. This can be relevant in the context of audio classification. It was successfully applied in the paperAudioCLIP: Extending CLIP to Image, Text and Audio ...
“radial viewing”), with the observation height defined with reference to the highest point of the coil. Currently, however, most commercially available systems make use of an emission observation along the axis of the torch (so called “axial viewing”). Recently commercial instruments tend to ...
shape[:2], device=device, dtype=dtype) * mask_param min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value) # Create broadcastable mask mask_start = min_value[..., None, None] mask_end = (min_value + value)[..., None, None...
index =torch.argmax(x, axis=0) y = torch.take_along_dim(x, index) print(y) tensor([0, 0, 1]) 最大值与最小值: aminmax()/clip()/clamp() torch.aminmax(input, *, dim=None, keepdim=False, out=None) 返回指定维度的最大值和最小值。
torch.histc(input, bins=100, min=0, max=0, out=None) → Tensor torch.meshgrid(*tensors, **kwargs)[source] torch.renorm(input, p, dim, maxnorm, out=None) → Tensor torch.repeat_interleave() torch.repeat_interleave(repeats) → Tensor ...
should compute. # This is done in a grouped ordering to promote L2 data reuse. # See above `L2 Cache Optimizations` section for details. pid = tl.program_id(axis=0) num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_in_group = GROUP_SIZE_...